Automatic quality checks for radiotherapy contouring

ABSTRACT

Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

FIELD

The present disclosure relates generally to radiation therapy, and moreparticularly, to systems devices, and methods for automated verificationof contours.

BACKGROUND

Radiosurgery and radiotherapy play an important role in the treatment ofcancers. In general, radiosurgery and radiotherapy treatments consist ofseveral phases. First, a precise three-dimensional (3D) map of theanatomical structures in the area of interest (head, body, etc.) isconstructed to determine the exact coordinates of the contour within theanatomical structure, namely, to locate the tumor or abnormality withinthe body and define its exact shape and size. Second, a motion path forthe radiation beam is computed to deliver a dose distribution that thesurgeon finds acceptable, taking into account a variety of medicalconstraints. During this phase, a team of specialists develop atreatment plan using special computer software to optimally irradiatethe tumor and minimize dose to the surrounding normal tissue bydesigning beams of radiation to converge on the contour area fromdifferent angles and planes. The third phase is where the radiationtreatment plan is executed. During this phase, the radiation dose isdelivered to the patient according to the prescribed treatment planusing radiation treatment techniques, such as intensity-modulatedradiation therapy (IMRT) and volumetric modulated arc therapy (VMAT),for example.

These techniques are typically used with a radiotherapy system, such asa linear accelerator (linac), equipped with a multileaf collimator (MLC)to treat pathological anatomies (tumors, lesions, vascularmalformations, nerve disorders, etc.) by delivering prescribed doses ofradiation (X-rays, gamma rays, electrons, protons, particles, and/orions) to the pathological anatomy while minimizing radiation exposure tothe surrounding tissue and critical anatomical structures.

Accurate delineation, also known as segmentation or contouring, oftargets, tumors, organs at risk (OAR), for example, is essential intreatment planning. Accurately delineating the targets, tumors, andorgans at risk, is an important factor in preventing geographic missesin radiotherapy planning. For example, an underestimation of tumorextension will result in tumor recurrence. In contrast, overestimationof the tumor extension may increase unnecessary side effects.

Accurate delineation of contours requires the identification of anatomicborders of the contours such as tumors and OARs based on accuratediagnosis. Currently, delineation (contouring) can be done manually,semiautomatically, or automatically. While such “contouring” istypically reviewed by trained staff, a risk remains that errors areintroduced and passed on to the next steps in the radiotherapy planning.

BRIEF SUMMARY

The embodiments disclosed herewith provide systems, devices, and methodsfor automatic detection of contouring errors to increase the efficiencyof the radiotherapy planning process and also improve patient safety.The embodiments disclosed herewith also provide a set of contourevaluation methods that can be automatically performed by a computerprocessing device in order to rule out a number of segmentation errors.

In an embodiment, a method is provided for automatically detectingsegmentation (contouring) errors by generating one or more segments(contours), and automatically evaluating the one or more segments(contours) using a heuristic evaluation method and/or a statisticalevaluation method. The evaluation of the one or more contours by aheuristic evaluation method comprises setting a set of heuristic rules,and determining whether at least one of the heuristic rules has beenviolated. If at least one of the heuristic rules has been violated, adetermination can be made that there is an error in the contour. Thedetermination of whether a heuristic rule has been violated can be doneby evaluating the one or more segments using Boolean operations andpixel/voxel counting, and/or by evaluating the one or more segments(contours) using a 3-dimensional (3D) connectivity labeling process.

The Boolean operation method can include generating binary images forthe segments, combining the binary images using at least one of aplurality of Boolean operators to generate a resulting binary image, anddetermining whether the resulting binary image satisfies the heuristicrule by counting pixels/voxels in the resulting binary image. If thenumber of pixels/voxels is judged to not be within a predeterminedrange, it is concluded that the heuristic rule has been violated, whichwould indicate that an error is present in the one or more contours.

The evaluation of the one or more contours by the statistical evaluationmethod includes evaluating a shape of the contour and determiningwhether the shape of the contour is within a predetermined range ofknown shapes for the contour. The evaluation method comprises generatinga shape model based on a probabilistic distribution of shape variationsfound in the range of known shapes for the contour, and performing astatistical test to check if the shape of the contour is within thepredetermined range. The probabilistic distribution can include amultivariate normal distribution

(μ, Σ).

The performing of the statistical check can also comprise evaluating howwell the contour shape fits within the multivariate normal distributionusing a probabilistic principal component analysis (PPCA). If the shapeof the contour does not fit within the multivariate normal distributionwithin a predetermined probability, it is determined that a segmentationerror is present.

Regardless of the evaluation method used, once it is determined that acontouring error is present, a warning signal alerting that asegmentation error is present can be displayed on a display device ofthe processing system, or the treatment planning system, or any otherdisplay device connected to an output of the segmentation errordetermination device. A segmentation (contouring) report including theresults of the segmentation (contouring) evaluation can also bedisplayed or printed.

In other embodiments, the determination whether at least one of theheuristic rules has been violated can also include evaluating the one ormore segments (contours) using a three-dimensional (3D) connectivitylabeling process.

The evaluation of the segments (contours) can be done in real time.

Systems for automatically performing the checking for errors insegmentation (contouring) are also disclosed. In one embodiment, thesystem comprises an imaging device configured to generate one or moreimage slices of a contour, and to mark segments (contours) of one ormore contour on the image slices, and a computer processing deviceconfigured to automatically evaluate the segments (contours) using aheuristic and/or a statistical evaluation method.

The computer processing device can comprise a database including a setof heuristic rules, wherein the automatic evaluation includes checkingwhether one or more of the segments (contours) are violating at leastone of the heuristic rules. The computer processing device can befurther configured to generate binary images for the segments (contours)and to combine the binary images using one or more Boolean operators.The computer processing device can be further configured to generate abinary image which is a result of the combining of the binary imagesusing the one or more Boolean operators, and evaluate whether theresulting binary image complies with a heuristic rule using apixel/voxel counting process.

The system can further comprise a display device to display an errorsignal if it is determined by the computer processing device that theresulting binary image fails to comply with a heuristic rule. The systemcan further display an error message if it is determined by the computerprocessing device that the contour (i.e., organ, for example) shape isnot within a range of known contour (organ) shapes.

In embodiments, the computer processing device is further configured toimplement a statistical shape model to determine if the shape of thecontour fits within a predetermined range of shapes for this contour.

In embodiments, a non-transitory computer readable medium containingprogram instructions for automatically evaluating segments (contours) isalso disclosed, wherein execution of the program instructions by acomputer processing device causes the computer processing device tocarry out the evaluation steps recited above, and disclosed herein.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments will hereinafter be described with reference to theaccompanying drawings, which have not necessarily been drawn to scale.Where applicable, some features may not be illustrated to assist in theillustration and description of underlying features.

FIG. 1 illustrates a computed tomography (CT) system according to one ormore embodiments of the disclosed subject matter.

FIG. 2 illustrates manual contouring of an image slice according to oneor more embodiments of the disclosed subject matter.

FIG. 3 illustrates a semiautomatic contouring of a CT image sliceaccording to one or more embodiments of the disclosed subject matter.

FIG. 4 illustrates a segment error detection process according to one ormore embodiments of the disclosed subject matter.

FIG. 5 illustrates an example of a segmentation of an organ at risk witha missing slice.

FIG. 6 illustrates an example of a segmentation of an organ at risk withan unintentionally placed small contour outside the organ.

FIG. 7 illustrates examples of segmentations with single and multipleoverlaps.

FIG. 8 illustrates an example of a segmentation with a CTV structurethat is not fully covered by a PTV structure.

FIG. 9 illustrates a shape model that uses example data to represent theshape variation of an organ, according to embodiments of the presentinvention.

FIG. 10 illustrates an example of using shape model fitting to assess agiven segmentation, according to embodiments of the present invention.

FIG. 11A illustrates an example of a segmentation to which a shape modelis applied, according to embodiments of the present invention.

FIG. 11B illustrates an example of a shape model to be applied to thesegmentation of FIG. 11A.

FIG. 11C illustrates the best fit model applied to the segmentation ofFIG. 11A using the shape model of FIG. 11B.

FIG. 12A illustrates an example of a segmentation to which a shape modelis applied, according to embodiments of the present invention.

FIG. 12B illustrates an example of a shape model to be applied to thesegmentation of FIG. 12A.

FIG. 12C illustrates the best fit model applied to the segmentation ofFIG. 12A using the shape model of FIG. 12B.

DETAILED DESCRIPTION

The primary aim of radiotherapy planning is to maximize radiation doseto a patient's tumor while sparing normal tissues and organs at risk. Toachieve this, the boundary of the tumor needs to be accuratelyidentified. Tumor delineation typically includes delineation(contouring) of gross, clinical, and planning target volumes for astructure of interest. The gross tumor volume (GTV) is the volume of allgross sites of disease (primary, nodal, and extramural vascularinvasion). The clinical target volume (CTV) encompasses areas ofmicroscopic spread beyond the defined GTV. The clinical target volume(CTV) includes two distinct volumes, CTVA and CTVB, with CTVA includingGTV+˜1 cm, for example, to define the surrounding safety margin ofpotential subclinical involvement (superior, inferior, anterior, andposterior), and CTVB including the organs at risk of involvement. Thefinal clinical target volume (CTVF) is a product of combining CTVA andCTVB. The planning target volume (PTV) is defined as CTVF+˜1 cm,(superiorly, inferiorly, anteriorly, and posteriorly), for example, toensure coverage of the CTV taking into account the systematic and randomsetup errors, changes over time in the patient geometry and internalorgan movement that may occur when delivering a course of radiation. Anyother combinations of target volumes, such as, the combination of grosstumor volume GTV, the clinical target volume CTV, and the planningtarget volume PTV, for example, can also be used for tumor delineationduring the planning. Besides target volumes, any other structures ofinterest, such as different organs at risk, for example, can also bedelineated during the planning.

Tumor delineation is performed by physicians, either manually or usingsemi-automatic/automatic software based on images of the patientobtained using an imaging method, such as, but not limited to, computedtomography (CT), positron emission tomography (PET), and/or magneticresonance imaging (MRI), for example. Delineating the structures ofinterest, such as the radiation target volumes, as well as the organs atrisk (i.e., organs that should be spared from radiation), involvesmarking or “segmenting” the structures of interest on each image sliceobtained using the CT, PET and/or MRI images. Then, the contours arevisually evaluated by trained medical staff. Although the contours arereviewed by trained staff, errors due to interobserver variation may beintroduced and passed on to the next steps in radiotherapy planning. Ifundetected, these errors will propagate throughout the planning phase,and it will ultimately affect the patient. Typical examples of sucherrors are missing slices in the segmentation of an organ at risk andunintentionally placed small contours outside the organ. FIG. 5illustrates an example of a segmentation of an organ at risk with amissing slice, and FIG. 6 illustrates an example of a segmentation of anorgan at risk with an unintentionally placed small contour outside theorgan. These errors will propagate through later stages of radiotherapyplanning if they remain undetected.

The present embodiments provide systems, devices, and methods toautomatically detect contouring errors to increase the efficiency of theradiotherapy planning process and also improve patient safety. Theembodiments provide a set of contour evaluation methods that can beautomatically performed by a computer processing device in order to ruleout a number of segmentation errors.

In operation, prior to the contour evaluation process and prior to thetreatment planning phase, a precise three-dimensional (3D) map of theanatomical structures in the area of interest (head, body, etc.) isconstructed using any one of a computed tomography (CT), cone-beam CBCT,magnetic resonance imaging (MRI), positron emission tomography (PET), 3Drotational angiography (3DRA), or ultrasound techniques. This determinesthe exact coordinates of the contour within the anatomical structure,namely, locates the tumor or abnormality within the body and defines itsexact shape and size.

In an exemplary embodiment, a computed tomography (CT) system 100, asshown in FIG. 1, is used to generate the images for the 3D map. Amotorized platform (table) 10 moves the patient 20 through the circularopening of the gantry 30 of the CT imaging system 100. As the patient 20passes through the CT imaging system, a source of x-rays 40 rotatesaround the inside of the circular opening. A single rotation can takeabout 1 second. The x-ray source 40 produces a narrow, fan-shaped beamof x-rays used to irradiate a section of the patient's body. Thethickness of the fan beam may be as small as 1 millimeter or as large as10 millimeters, for example. In examinations there are several phases,each made up of 10 to 50 rotations of the x-ray source 40 around thepatient 20 in coordination with the platform 10 moving through thecircular opening. The patient 20 may receive an injection of a contrastmaterial to facilitate visualization of vascular structure.

One or more detectors, such as one or more Electronic Portal ImagingDevices (EPIDs) 50, for example, on the exit side of the patient 20record the x-rays exiting the section of the patient's body beingirradiated as an x-ray “snapshot” at one position (angle) of the sourceof x-rays 40. Many different “snapshots” (angles) are collected duringone complete rotation. The data is then sent to a system controller 60including a computer processing device to reconstruct all of theindividual “snapshots” into a cross-sectional image (i.e., image slice)of the structures, such as the internal organs and tissues, for eachcomplete rotation of the source of x-rays 40.

The system controller 60 includes a processor that is integrated intothe imaging system, or could be a separate computer processing device 70that is connected to the imaging device 100. The controller 60 and/orthe computer processing device 70 can include a computer with typicalhardware, such as a processor, and an operating system for runningvarious software programs and/or communication applications. Thecomputer can include software programs that operate to communicate withthe imaging device 100. The software programs are operable to receivedata from external software programs and hardware. The controller and/orthe computer processing device 70 can also include any suitableinput/output devices adapted to be accessed by medical personnel, aswell as input/output (I/O) interfaces, storage devices, memory,keyboard, mouse, monitor, printers, scanners, etc. The controller 60and/or the computer processing device 70 can also be networked withother computers and radiation therapy systems. Both the imaging device100 as well as the computer processing device 70 can communicate with anetwork as well as a database and servers. The controller 60 and/or thecomputer processing device 70 can also be configured to transfer medicalimage related data between different pieces of medical equipment.

The imaging system 100, the system controller 60 and/or the computerprocessing device 70 can also include a plurality of modules containingprogrammed instructions (e.g., as part of the computer processing systemand/or the imaging system, or integrated into other components of theimaging system, or as separate modules within the imaging system), whichinstructions cause the imaging system to allow all image guidanceactivities, such as, image acquisition, image registration, imageinterpretation, image evaluation, capture of data needed for imageacquisition and evaluation, image transformation, image transfer,contour generation, contour extraction, contour evaluation, imagedisplay, contour display, and/or results display, as discussed herein,when executed.

The interface of the controller 60 and/or the computer processing device70 is configured to allow a user to input data, manipulate input andoutput data, and make any edits to the data, to the contour, and to thedisplayed output. The modules can be written in C or C++ programminglanguages, for example. Computer program code for carrying outoperations as described herein may also be written in other programminglanguages.

The cross-sectional images (i.e., image slices) of the variety ofstructures, such as the internal organs and tissues, for example,obtained during the imaging of the patient 20 using the CT system 100can be stored in a storage device of the controller 60, or a storagedevice of the computer processing device 70, or in an external storagedevice accessible by the system controller 60 and the computerprocessing device 70, for further processing. Although the images in theexemplary embodiment have been obtained using a CT system and an x-raysource, any other imaging systems and radiation sources may be used.

Once the image slices are obtained using the CT, PET, MRI, or any otherused imaging methods, different segmentation techniques can be appliedon the image slices to delineate a variety of structures (e.g., organs,anatomy, tissue, etc.). Such segmentation can be performed manually,semiautomatically or fully automatically. In embodiments, the contoursmay also be segmented on separately obtained images and transferred tothe main planning image.

Manual techniques, such as shown in FIG. 2, allow users to outlinestructures manually or using software, such as, but not limited to, theANALYZE software package. During manual segmentation, a user manuallydraws contours around different structures of interest, such as a tumor,organs, organs at risk, a tissue segment, etc. Although manualsegmentation may be accurate, it is time-consuming and tedious forusers. Also, manual segmentation causes interobserver variation or bias.

Semiautomatic techniques, such as shown in FIG. 3, allow the user tohave some control or input into the segmentation process, combined withsome automatic process using computer algorithms. Semiautomaticapproaches based on thresholding, region growing, and deformable models,can be used in numerous applications.

Fully automatic techniques require no user input and often make use ofsome prior knowledge from the anatomy being segmented to produce thesegmentation or delineation. Two examples of these approaches areatlas-based segmentation and statistical shape model segmentation.

There are many available toolkits provided by commercial software fordelineation, such as the contour tools provided by iPlan, Eclipse, andPinnacle, that can be used for contouring. For example, the iPlan RTimage (BrainLAB, Feldkirchen, Germany) provides automatic delineationfor structures, and fast contouring is achieved with unique, atlas-basedautomatic organ segmentation. In Eclipse treatment planning system(Varian Medical Systems, Palo Alto, Calif.), SmartAdapt contouring isachieved by automatically deforming and propagating initial contours tomatch the current anatomy, and editing or fine tuning the changes usinga variety of two-dimensional (2D) and three-dimensional (3D) contouredition features. This is a semiautomatic method that combines automaticcontouring and manual revision. On Pinnacle (Philips Healthcare,Andover, Mass.) contours are completed using autocontour andautothreshold tools. The model based segmentation (MBS) software ofPinnacle includes an anatomic library of 3D patient organ structuremodels, which reduces the time spent manually drawing contours. Any ofthe above described delineation methods, or other applicable delineationmethods, can be used to obtain the contours herein.

After the delineation, the image slices containing the contours arestored in a database of the controller 60 and/or the computer processingdevice 70 for contour evaluation. The image slices may be stored asdigital images.

In order to automatically evaluate the contours, a set of evaluationmethods are stored in the system controller 60 and/or the computerprocessing device 70 and/or the external storage device. When executedby the system controller 60 or the computer processing device 70, theset of evaluation methods automatically checks the contours marked oneach image slice to rule out a number of segmentation errors. Theexecution of the set of evaluation methods can be automaticallyinitiated after the segmentation process is finished, or can beinitiated by a user at will. The selection of the evaluation methodswithin the set, as well as the order of execution of the evaluationmethods can be done automatically without user input or can becontrolled by the user. The selection of the methods and/or the ordermay depend on different previously determined parameters, including butnot limited to, the type of the segmented structure, the type of imageobtained, the type of segmentation process used to obtain the contours,etc. The user inputs may be done using a user interface communicatingwith the computer processing device 70 and/or the system controller 60.

These automatic computerized evaluation methods include, but are notlimited to, heuristic evaluation methods and statistical shape modelevaluation methods.

Heuristic Evaluation Method:

Heuristics are rules of thumb for reasoning, simplification, andeducated guess that reduce or limit the search for solutions for acomplex problem. Since the problem that the present invention seeks tosolve, namely, to determine whether a given segmentation is correct, isa complex one, applying a heuristic evaluation method to solve theproblem allows for a simplified way to make a contour errordetermination.

The proposed heuristic evaluation method includes a set of rules thatare quality checks for testing whether the obtained contours adhere to aset of common-sense rules. These rules are predefined checks which areused to identify contouring errors. For example, one of the rules isthat the contoured organ must be a single connected structure with nomissing slices or accidentally misplaced contours. Another rule checksthat different contours of different organs do not overlap each other.Depending on the type of the structure of interest, different rules maybe applied. The rule parameters (e.g., thresholds) may also vary. Theexecution of the one or more rules within the heuristic evaluationmethod can be automatically initiated after the segmentation process isfinished, or can be initiated by a user at will. The selection of therules and/or combination of rules to be used, and the order of executionof the selected rules can be done automatically using a determinationengine to determine which rules are applicable and/or desired based on apreviously determined and compiled set of parameters, such as, but notlimited to, the type of the segmented structure. The selection of therules and/or combination of rules to be used, and the order of executionof the selected rules can also be done via user input, where the userselects from the set of rules which ones to be used, and in which order.The selection may depend on different previously determined parameters,including but not limited to, the type of the segmented structure, thetype of image obtained, the type of segmentation process used to obtainthe contours, etc. The user inputs may be done using a user interfacecommunicating with the computer processing device 70 and/or the systemcontroller 60.

The following heuristic rules can be applied in one or more embodimentsdisclosed herein. This list is only exemplary, and other heuristic rulesmay also be applied alone or in combination with any of the listedrules:

-   -   1) The structure must be inside the contour of the body;    -   2) The structure must not overlap with another structure;    -   3) The structure must not contain any empty slices;    -   4) The structure must be one single 3D connected contour;    -   5) Length or volume, or other quickly computed shape features of        the structure, must be in a predefined typical range;    -   6) The left and right paired structures, such as lungs, eyes,        kidneys, femurs, etc. for example, are not switched by mistake;    -   7) The CTV structures are completely covered by at least one PTV        structure;    -   8) The GTV structures are completely covered by at least one CTV        structure;    -   9) The organ at risk is contoured in a sufficient area around        the PTV.

Not all rules apply to each organ. For example, there are organs thatare not a single connected structure, and therefore some of these rulesdo not apply. The selection of specific rules that can be applied toeach organ and tissue may be based on predefined radiotherapy atlases,such as the radiotherapy atlas for the delineation of organs at risk fora thoracic radiotherapy described in “Consideration of Dose Limits forOrgans at Risk of Thoracic Radiotherapy: Atlas for Lung, ProximalBronchial Tree, Esophagus, Spinal Cord, Ribs, and Brachial Plexus,” byFeng-Ming (Spring) Kong et al., in International Journal of RadiationOncology*Biology*Physics, 2011, for example, which is incorporatedherein by reference in its entirety. Any other predefined radiotherapyatlases may be used for the selection of the heuristic check rules to beapplied for organs or tissues.

In an exemplary embodiment, a heuristic rule is applied to require thata structure is inside the contour of the body. In another exemplaryembodiment, a heuristic rule is applied to require that the structuredoes not overlap with another structure. In another exemplaryembodiment, a heuristic rule is applied to require that the structuredoes not contain any empty slices (e.g., as illustrated in FIG. 5). Inanother exemplary embodiment, a heuristic rule is applied to requirethat the structure is a single 3D-connected contour. In anotherexemplary embodiment, a heuristic rule is applied to require thatquickly computed shape features such as length or volume are in apredefined range. In another exemplary embodiment, a heuristic rule isapplied to require that the left and right lung structures are notswitched by mistake (i.e., the left lung lies to the left of the rightlung). In another exemplary embodiment, a heuristic rule is applied torequire that the clinical target volume (CTV) structures (e.g., thetumor plus an additional area for possible disease spread) arecompletely covered by at least one planning target volume (PTV)structure (e.g., CTV plus an additional area to account foruncertainties in planning/treatment delivery). An example of asegmentation with a CTV structure that is not fully covered by a PTVstructure is illustrated in FIG. 8.

In another exemplary embodiment, a heuristic rule is applied to requirethat gross tumor volume (GTV) structures (e.g., size and position ofimaged/seen gross tumors) are completely covered by at least one CTVstructure. In another exemplary embodiment, a heuristic rule is appliedto require that the organ at risk is contoured in a sufficient areaaround a PTV.

In exemplary embodiments, all applicable heuristic rules are used. Inother embodiments, one or more heuristic rules alone or in combinationmay be used.

The implementation of one or more heuristic rules is achieved based onpixel and/or voxel (i.e., a region in a slice that corresponds to apixel in an image) counting and Boolean operations (e.g., intersectionand subtraction, etc.) of structures. This can be done as follows:

The segments (contours) on each image slice obtained using one of thevariety of imaging applications (CT, PET-CT, MRI, ultrasound) areconverted into binary images. In some embodiments conversion is notneeded since the contours have already been represented as binaryimages. This depends on how the 3D segmentation software represents thecontours in the first place. Since the segments (contours) representsubsets of the 3D space, often only their surface is considered, namelya 2D surface in 3D space. The 3D segmentation software can choose one orseveral of the following contour representations.

-   -   1.) Polygonal meshes of the surface. This is most common in        computer graphics. A polygon mesh is a collection of vertices,        edges and faces that defines the shape of an object in 3D        computer graphics and solid modeling. The faces usually consist        of triangles (triangle mesh), quadrilaterals, or other simple        convex polygons, since this simplifies rendering, but may also        be composed of more general concave polygons, or polygons with        holes. In this representation, the segment (contour) is defined        as the 3D space inside the polygon mesh.    -   2.) Contours stacks. This is generally used in Digital Imaging        and Communications in Medicine (DICOM), which is a standard for        handling, storing, printing, and transmitting information in        medical imaging. It includes a file format definition and a        network communications protocol. The communication protocol is        an application protocol that uses TCP/IP to communicate between        systems. In DICOM, for each slice of the original image        (planning CT), a polygonal contour is stored. This contour        outlines all pixels on that slice that belong to the structure.        The 3D stack of all these pixels defines the segment (contour)        in this representation.    -   3.) Binary images or label maps. These are digital images that        have only two possible values for each pixel or voxel, usually        black and white. The values need not be binary, however. Values        such as 0 and 255 with a smooth transition in between can be        used. A signed distance map with value 0 on the surface and        negative values inside can also be used. Positive values can        also be used. In a label map, more than one segment can be        represented in one image. In this representation, the segment        (contour) is defined as the set of voxels that have a specific        value or value range. In the simplest case, all white voxels        belong to the segment (contour) while all black voxels belong to        the background.

In order to convert contours into binary images, in DICOM, for example,where a segment is stored as a stack of closed 2D contours, one 2Dcontour for each slice of the image the contours are meant to annotate,for each voxel in the associated 3D image, it is determined whether thevoxel is inside or outside the contour, and a pixel value is assignedaccordingly. By analyzing the pixel values of the original imagescontaining the contours and assigning values to the pixels in eachimage, a plurality of binary images are obtained, in which all pixels(voxels) with a value above the threshold (i.e., value “1”) areconsidered to be within the structure (contour), and all pixels (voxels)with a value below the threshold (i.e., value “0”) are considered to beoutside of the structure (contour). The computer processing device mayanalyze the image data and assign a “1” or a “0” value or theirequivalents on the fly. The contours within each image are nextextracted using any applicable contour extraction processes. Eachcontour within an image is then saved as a binary image in a storagedevice associated with the system controller 60 and/or the computerprocessing device 70, or the external storage device, for furtherprocessing.

The binary contour images are next combined using Boolean operations andpixel counting. Boolean operations include a set of logical rules bywhich binary images can be combined. The four basic Boolean operationsare AND, OR, Ex-OR (Exclusive OR) and NOT. Boolean operations can alsobe combined for more complex image combinations. The operations areperformed pixel-by-pixel.

For example, given two binary images A and B, the Boolean combination (AAND B) generates a single image C as a result. The image C will containonly the pixels that have a value “1” in both the A and B images. ABoolean combination (A OR B) generates a single image C as a result,where image C will contain the pixels which have a value “1” in eitherthe A or the B image. A Boolean combination (A Ex-OR B) also generates asingle image C as a result, containing the pixels which have a value “1”in either image A or B, but not if the pixels have a value “1” in bothimages A and B. A Boolean combination (A NOT B) requires only a singleimage A or B, and the result is an image C where the pixels arereversed, namely, all pixels that had a value “1” in the original imagewill have a value “0” in the generated image, and the pixels that had avalue “0” in the original image will have a value “1” in the generatedimage. A Boolean combination ((NOT A) AND B) will produce an image Ccontaining pixels that lie within B but outside A. A Boolean combination(NOT (A AND B)) generates an image C containing pixels that do not havea value “1” in both A and B. In a Boolean operation (A-B), the resultingbinary image C is an image that contains the volume of the structure ofA with the intersecting volume of structure of B removed.

Using Boolean logical rules, any number of binary contour images can becombined (two at the time), and the resulting image analyzed in order todetermine whether a heuristic rule has been complied with or has failed(i.e., rule was violated).

For example, if the heuristic rule is that the structure must notoverlap with another structure (i.e., one contour must not overlapanother contour), the following steps are taken to check whether thisrule has been complied with:

1. The computer processing device 70 fetches two binary contour images Aand B from the storage device to be analyzed;

2. The binary contour images A and B are combined using a Booleancombination which computes the intersection between the two contours(i.e., Boolean operation (A AND B)). The resulting binary image C is animage that contains the pixels that are assigned a value “1” in bothimages A and B. This image C will thus include the pixels that arecommon to both A and B, and thus include the pixels where the twocontours intersect. This image C is thus an image of the intersection ofthe two contours;

3. The pixels (voxels) in the image C, namely, the pixels/voxels in theoverlapping region are then counted. The counting can be done using anyapplicable counting methods. One way of counting the pixels/voxels is byperforming a series of one directional passes across the rows andcolumns of image C to search for the pixels/voxels having a value “1”and storing a count for each of these pixels/voxels;

4. If the number of counted pixels (voxels) is more than zero, adetermination is made that an overlap exists;

5. The volume of overlapping region is computed by computing the numberof voxels times the voxel size;

6. In order to not report insignificant overlaps (i.e., if the overlapis so small that it is insignificant), the volume of overlapping regionis compared with a predetermined threshold volume;

7. If the volume of overlapping threshold region is above the thresholdvolume, a determination is made that there is a significant overlapbetween the structures of image A and image B. Since the heuristic rulerequired that there be no overlap between the structures, adetermination is made that the heuristic rule has been violated;

8. A user is alerted that a quality check has failed.

In addition, or alternatively, if the heuristic rule is that the CTVstructure is completely covered by a PTV structure (i.e., the PTVcontour must completely cover the CTV contour), the following steps aretaken to check whether this rule has been complied with:

1. The computer processing device fetches two binary contour images Aand B to be analyzed;

2. The binary contour images A and B are combined using Booleancombination which computes the subtraction between the two contours(i.e., Boolean operation (A-B). The resulting binary image C is an imagethat contains the volume of one structure A with the intersecting volumeof structure B removed, and thus it contains the pixels of structure Athat is not covered by the structure of B;

3. The pixels (voxels) in the resulting image C are counted. Thecounting can be done using any applicable counting methods. One way ofcounting the pixels is by performing a series of one directional passesacross the rows and columns of image C to search for the pixels/voxelshaving a value “1” and storing a count for each of these pixels/voxels;

4. If the number of counted pixels (voxels) is more than zero, adetermination is made that the structure of image B is not completelyoverlapping the structure of image A;

5. The volume of not-overlapping region is computed by computing thenumber of voxels in the not-overlapping region times the voxel size;

6. In order to not report insignificant no-overlaps (i.e., if thenon-overlapped region is so small that it is insignificant), the volumeof non-overlapping region is compared with a predetermined thresholdvolume of non-overlapping region;

7. If the volume of non-overlapping threshold region is above thethreshold volume of non-overlapping region, a determination is made thatthe structure of B is not completely overlapped by the structure of A,and thus the heuristic rule has been violated;

8. A user is alerted that a quality check has failed.

The same functionality (i.e., determining subtraction (A-B) and thencounting voxels) may also be used to check the rules “CTV must coverGTV” and “a structure must be inside the contour of the body.”

For the heuristic rule “the structure does not contain any emptyslices,” for each image slice (A, B, etc.) the number of voxels that areassigned a value “1” are counted. If the number of counted pixels(voxels) is above a threshold pixel/voxel number, a determination ismade that the image slice is not empty. If the counted pixel/voxelnumber is less than the predetermined threshold number, a determinationis made that the slice is empty, and thus the heuristic rule has beenviolated.

For the heuristic rule “the length of the shape must be in a predefinedtypical range,” a scan-line approach may be used to pass over the image,identify the voxels that are over a threshold, determine the largestdistance between voxel pairs among such voxels, and compare the distanceto a threshold length. If the obtained distance is greater or smallerthan the threshold length, it is determined that the length of the shapeis not within a predefined range.

For the heuristic rule “the volume of the shape must be in a predefinedtypical range,” the voxels of the shape are counted, the voxel count ismultiplied by the voxel size, and the result is compared with athreshold volume range. If the volume calculated is outside thepredetermined threshold volume range, it is determined that the volumeof the shape is not within a predefined range.

For the heuristic rule “the left and right lung structures must not beswitched by mistake”, the left and right lungs are two separatestructures. For each, the center of gravity can be computed. Thereafter,by comparing the position of the centers of gravity of the right andleft lungs, it is determined whether they are switched.

In operation, the plurality of binary contour images obtained from thegenerated image slices (CT, MRI, etc. image slices, for example) areautomatically evaluated using a heuristic evaluation method by which twoor more images are combined based on one or more heuristic rules usingone or more Boolean operations, and the pixels and/or voxels in theresulting image are counted. The number of pixels/voxels in theresulting image determines whether one or more of the heuristic ruleshave been violated.

In embodiments, a check failure signal can be sent after at least oneheuristic rule has been violated. In other embodiments, a check failuresignal can be sent after two or more heuristic rules have been violated.In alternative embodiments, one or more heuristic rules are implementedfor each quality check.

Although the embodiments have been described using binary images, theheuristic check can also be applied on contours that have not beenconverted into binary images.

In addition to, or alternatively, the heuristic rules can be checked byperforming a 3D connectivity labeling technique. The 3D labelingtechnique identifies disconnected parts of a segment (contour) byperforming a 3D connectivity labeling process which labels all connectedcomponents of a structure. When 3D labeling is applied, the qualitycheck fails if more than one component is found for structures thatshould include only one component.

3D connectivity labeling can also be applied if there is one segmentthat includes multiple components, such as the lung, which consists ofthe left and right lung. In such a case, 3D labeling will return thenumber 2 (for two labels) and will label one lung with value 1 and theother with value 2.

3D labeling can also use a scan-line approach to pass over an image twotimes, propagating the labels of the segments that have already beenencountered. When a new segment is encountered, a new label is added.When labels meet, it means that they actually belong to the samesegment, and a method is called to merge the two labels into one label(e.g., by changing both labels to a common new label or by changing bothlabels into one of the two labels). The same labeling technique may beused to identify if a heuristic rule has been violated in several placesand/or to determine in how many spots a quality check fails. Forexample, when an overlap is detected between two structures (segments),a 3D connectivity labeling may be performed to identify if there areseveral individual/disconnected overlaps between two structures (e.g.,as illustrated in FIG. 7), and the overlaps can be presented to the userindividually.

In operation, the plurality of binary contour images obtained from thegenerated image slices (CT, MRI, etc. image slices, for example) areautomatically evaluated using a heuristic evaluation method which isbased on performing a 3D connectivity labeling process.

In some embodiments, depending on computational complexity, some or allquality checks are executed on-the-fly as a user edits the contours.

In some embodiments, an alert signal/message can be displayed on adisplay mechanism of the computer processing device 70 to alert the userof failed quality check. The user may further expand the alert messageto see additional details about why and where the quality checks failed.Options to focus the view on the place where the quality checks failed(e.g., showing the location where two structures overlap) and/orproviding options to correct the error (e.g., interpolating missingslices, etc.) can also be included.

In some embodiments, the evaluation method is performed in real time,and thus the contours can be evaluated in real time. In addition, oralternatively, in some embodiments, a contouring report may begenerated. The contouring report may provide an overview of the currentstate of contouring, including statistics about the patient, planningimages, and results of the quality checks. Upon review of the report, auser may immediately respond to a failure of one or more quality checks,either by redoing the contouring, for example, in order to improve theefficiency and safety of the structure review process by highlightingany potential issues.

Statistical Shape Models Evaluation Method:

Statistical shape model evaluation methods are quality checks thatperform statistical tests based on previous reliable segmentations ofthe same organ. Using previous segmentations of sufficient quality andquantity, a “statistical shape model” is built. This model is aprobability distribution of shape variations found in the examplesegmentations. A statistical test tailored to these models may then beperformed to check if the shape of a given structure, such as an organfor example, lies within the normal range known for this type ofstructure/organ.

Alternatively, or in addition to the heuristic evaluation method, thecontours can be further automatically evaluated using a statisticalshape model evaluation method. This can be done by implementing astatistical shape model evaluation method by the computer processingdevice 70. This evaluation method defines a probability distribution tomodel the normal variation of the shape of an organ. In one embodiment,this distribution is a multivariate normal distribution

(μ, Σ) defined over the space

p of deformations of a predefined reference organ x₀. The parameters ofthe multivariate normal distribution

(μ, Σ) are the mean μ and the covariance matrix Σ that are estimatedfrom a set of examples x₁, . . . , x_(n) as:

$\mu = {{\frac{1}{n}{\sum\limits_{i = 1}^{n}{x_{i}\mspace{50mu}\sum}}} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}{( {x_{i} - \mu} )( {x_{i} - \mu} )^{T}}}}}$

FIG. 9 illustrates a shape model that uses example data to represent theshape variation of an organ. Depending on the type of model used, thespace of deformations may be defined over only the shape x₀ or over itssurrounding space

p. In one embodiment, in order to model the example shapes asdeformations of a reference shape, the example shapes need to be broughtinto correspondence by “non-rigid registration” (due to biologicaldifferences and/or due to image acquisition, localized stretching ofimages is needed in order to achieve correspondence between structuresin two images). The non-rigid registration algorithm computes theexample deformations x₁, . . . , x_(n) from which the shape modelparameters can be estimated as shown above.

A principal component analysis (PCA) can be performed in order toefficiently store and handle a shape model. PCA is a statisticalalgorithm that implements an orthogonal transformation to convert a setof observations of possibly correlated variables into a set of values oflinearly uncorrelated variables called principal components. PCAessentially provides a diagonalization of the covariance matrix Σ. Theterm “RCA models” commonly refers to statistical shape models. In orderto construct a statistical shape model, a mean shape and a number ofmodes of variation from a collection of training samples (e.g., previoussegmentations of an organ) can be extracted. Some examples ofstatistical shape models as PCA models that can be used in the disclosedembodiments are disclosed in “Statistical shape models for 3D medicalimage segmentation: a review,” T Heimann et al., in Medical imageanalysis, 2009, which is incorporated herein by reference in itsentirety.

In order to check if a given new segmentation of an organ fits the shapemodel built from previous segmentations of that organ, it is determinedhow probable the new segmentation is under the multivariate normaldistribution

(μ, Σ) built from previous segmentations. In order to do so, in oneembodiment the probability density distribution of the multivariatenormal distribution

(μ, Σ) is directly evaluated. In another embodiment, the statisticalshape model is used to compute the probability of observing asegmentation that is as probable or less probable than a givensegmentation. This can be computed from the statistical shape model witha statistical test and quantified with a probability value (p-value)between 0 and 1. As such, a threshold value (e.g., 5%, for example) canbe chosen below which a segmentation is deemed too improbable to accept.Alternatively, the p-value can be displayed to the user to judge theprobability they are willing to accept.

An example of a statistical test is disclosed in “Process monitoringbased on probabilistic PCA (PPCA),” Dongsoon Kim et al., Chemometricsand Intelligent Laboratory Systems, 2003 (hereinafter “Kim”),incorporated herein by reference in its entirety, where the statisticaltest is based on PPCA and the objective of the statistical test isdetecting abnormal events of a process with respect to a process model.Such a statistical test based on PPCA for detecting abnormal shapes withrespect to a shape model, can be applied in embodiments of the disclosedevaluation method.

PPCA is an extension to the PCA models described above, and, in additionto the normal shape variations described by the PCA model, models theadmissible deviation from the model by white-type noise with negligibleamplitude or variance, which may result in abnormal means of variables,abnormal variance of variables, and/or abnormal correlations amongvariables. The abnormality may be judged by statistical testing for theelements of a given segmentation. PPCA is based on a probabilisticgenerative model where the measurement variable x is assumed to be theoutput of the linear combinations of mutually uncorrelated inputvariable z plus additive noise e, and where some probability densitiesare specified for the variables in the model. PPCA aims to find the mostprobable parameter set:Θ≡{A,λ}in the model structure:x=A·z+ewhere A is the loading matrix and A is the covariance matrix of e whichis Gaussian with zero mean. In one embodiment, the parameter set can beestimated by the “expectation and maximization” algorithm, which is aniterative likelihood maximization algorithm.

A given segmentation needs to be available in a suitable mathematicalrepresentation in order to assess it with a statistical shape modeltest. That is, a given segmentation needs to be represented as adeformation of the model's reference organ. In order to do that, oneembodiment implements functionality similar to the building phase of themodel to achieve such representation of the given segmentation withnon-rigid registration. This registration implements an optimizationalgorithm that finds a deformation of the reference that best matchesthe given segmentation. In order to make the optimization more efficientand robust, “model fitting” is implemented and the search space of theoptimization is restricted to the span of the statistical shape model.The result can then be assessed with a statistical test (e.g., with thestatistical test of Kim, for example).

In an exemplary embodiment, “model fitting” includes the process offinding the shape model parameters which best explain the given data. Itis an optimization procedure which minimizes the distance between thecurrent model instance and the given data. Model fitting is applicableto the present embodiments since the PPCA model is a generative model,which means it can generate instances of the model, which can bedirectly compared to the input data. In the aforementioned modelstructure, this means finding z so that A·z is as close to x aspossible. Because the model is built from a finite set of example datasets, the term A·z may not perfectly represent the infinite variabilityof every possible input data, no matter if it is the correct type oforgan or not. In the PPCA model, this is modeled by the noise term e. Soeven if the optimization has found a set of parameter z so that A·z isvery close to x, a residual may remain. Accordingly, the statisticaltest may check two sets of values: a within-model probability (i.e., theprobability of the best fit within the span of the model) and a residualbetween the model fit and the given segmentation. That is, one part ofthe test is to determine if this residual is small enough to beexplained by the noise variable e (i.e., the probability of theresidual), and the other term that needs to be tested is the set ofmodel parameters z.

For example, even if A·z is very close to x, the parameters computed bythe optimization to achieve this close fit may be very extreme. So thesecond test is to determine if these variables are in accordance withthe PPCA model (i.e., the within-model probability). The matrix A in thePPCA model is configured so that the variables z are N (0, I)distributed, which means each variable has a 1D normal distribution withno correlation between the variables. Similarly, the noise variable emodels only uncorrelated Gaussian noise. Due to this uncorrelated model,each variable's contribution to the statistical test can be assessedindividually. These two checks (i.e., related to within modelprobability and residual) may be aggregated or may be consideredindividually.

The spatial relation of organs can be further modelled as disclosed in“Automated contouring error detection based on supervised geometricattribute distribution models for radiation therapy: A generalstrategy,” Hsin-Chen Chen, et al., Medical Physics, 2015 (hereinafter“Chen”), which is incorporated by reference herein in its entirety. Thismodeling models the relative spatial relation of several organs in a“geometric attribute distribution” (GAD), which is a model of centroid,volume and shapes of a set of organs. The shape characteristics of theindividual organs are modeled by statistical shape models that are basedon linear combinations of signed distance maps. Accordingly, when suchfurther modeling is applied, a further statistical modeling of therelative position of the organs at risk with respect to each other isperformed. Alternatively, the relative position of the organs at riskwith respect to each other may be checked with heuristic rules.

In operation, in addition to, or as an alternative to the heuristicevaluation method, a segmentation (i.e., contour) can be furtherevaluated using a statistical shape model evaluation method. In order todo that, first the segmentation is converted to a mathematicalrepresentation, namely, the segmentation is represented as a deformationof the model's reference organ. Then the deformation is assessed using astatistical test, such as a probabilistic PCA test, to determine if agiven structure (contour) is in accordance with the statistical shapemodel. If it is, then it is determined that the shape of the organ lieswithin the normal range known for this type of organ. If it is not, thenit is determined that the shape of the given organ lies outside of thenormal range known for this type of organ, and therefore the contourcontains errors. In such an instance, a user may be alerted that thequality check has failed. The user may further expand the alert messageto see additional details about why and where the quality checks failed.Embodiments also provide options to focus the view on the place wherethe quality checks failed (e.g., showing the location where thedifference between the segmentation and the shape model is largest)and/or provides options to correct the error (e.g., interpolatingmissing slices).

Depending on computational complexity, some quality checks (heuristicand/or statistical) are executed on-the-fly as a user edits thecontours. Further, the statistical and/or heuristic quality checks canbe provided in real time. A contouring report may also be displayed toprovide an overview of the current state of contouring, includingstatistics about the patient, planning images, and results of thequality checks.

An automatic real time segment (contour) error evaluation method 200 isillustrated in FIG. 4. In step S1, one or more segments (contours) aregenerated. This can be done using any of the available manual,semiautomatic and automatic contouring tools and methods on each of thegenerated CT slices. In S2, the segments (contours) are automaticallyevaluated by a computer processing device using a heuristic evaluationmethod and/or a statistical evaluation method. The heuristic evaluationmethod includes checking whether one or more of a set of heuristic rulesimplemented using Boolean operators and pixel/voxel counting and/or a 3Dconnectivity labeling process have been violated.

The statistical evaluation method includes using a statistical shapemodel evaluation method to assess, using a statistical test, such as aprobabilistic PCA test, for example, if a given shape of an organ lieswithin the normal range known for this type of organ. In S3, anauthorized personnel is notified that at least one of the heuristicrules and/or the statistical rule is violated, which is an indication ofan error in either the segment (contour) and/or an error in the shape ofthe contour (i.e., organ).

FIGS. 11 and 12 illustrate exemplary quality checks performed ondifferent input segmentations (contours) using a statistical evaluationmethod. The segmentation (contour) B shown in FIG. 11A is that of abrachial plexus including a right side segmentation portion B2 and aleft side segmentation portion B1 (i.e., their surface representations).The quality check performed on segmentation B includes checking if thesegmentation B fits a corresponding shape model's (i.e., shape model Mshown in FIG. 11B) probability distribution

(μ, Σ). The shape model M shown in FIG. 11B represents the model mean(e.g., the mean of a set of previous segmentations of the same organ).FIG. 11C shows the result of the model fitting of segmentation B inaccordance with the shape model distribution defined by

(μ, Σ). The best fit is the model instance according to the computedparameters z. It is clearly shown in FIG. 11C that the model M can befitted to the input shape (i.e., segmentation B) quite well and theresidual is small. But the within-model probability of the modelparameters z is still low because the parts of the shape that extend tothe left (P1) and the right (P2) are too small. Because the qualitycheck has detected that the left and right protrusions P1, P2 of theshape are too small, the segmentation (contour) B fails quality check.If the protrusions P1, P2 would have fit within a previously determinedacceptable size range, the quality check of segmentation B would havepassed.

The input segmentation (contour) C shown in FIG. 12A is also that of abrachial plexus. In this case, however, there is only a left sidesegmentation portion C1, with the right side segmentation portion C2missing. The quality check performed on segmentation C also includeschecking if the segmentation C fits a corresponding shape model's (i.e.,shape model M shown in FIG. 12B) probability distribution

(μ, Σ). The shape model M shown in FIG. 12B represents the model mean(e.g., the mean of a set of previous segmentations of the same organ).FIG. 12C shows the result of the model fitting of segmentation C inaccordance with the shape model distribution defined by

(μ, Σ). It is clearly shown in FIG. 12C that because the input shape(i.e., segmentation C) is missing half of the brachial plexus (i.e.,missing segmentation C2), the fitting is not really possible, resultingin both a high residual and a very improbable model parameters z.Therefore, the segmentation (contour) C fails quality check.

In embodiments, the heuristic quality checks are configured to identifycommon mistakes, such as missed slices and misplaced contours, withefficient and clear heuristic rules. In embodiments, the use of accurateshape models that are based on point correspondence and registration,are implemented to statistically evaluate the shapes of structures underevaluation.

Some or all of the quality checks of the present embodiments can be usedin other applications in which manual or automatic segmentation ofimages (2D, 3D, and/or 4D) is performed and the segmentations need to beevaluated and controlled. Examples of such applications are medicalimage segmentation for operation planning, segmentation for quantitativemeasurements, etc.

Some or all of the quality checks of the present embodiments can also beused to evaluate the plurality of segmentations used to generate asegmentation model, and therefore perform an overall quality check ofthe segmentation model prior to be used to evaluate a contour. Forexample, for a segmentation model M generated based on previouslyobtained plurality of segmentations (contours), a heuristic evaluationcan be applied to verify that the segmentations pass the appliedheuristic rules. If the segmentations, or if a predefined percentage ofthe segmentations pass the heuristic evaluation, it can be concludedthat the segmentation model M, which may be used for the statisticalevaluation of a contour at a later time, is indeed correct.

Embodiments are also applicable even when segmentation of the objects istrivial (e.g., in photographs of produced or manufactured goods) wherethe quality checks of the embodiments can be employed to control if thegiven object adheres to predefined standards. One such application isagricultural or industrial quality control.

Embodiments are further applicable in data mining when all structures ofa certain kind need to be extracted from a database for further analysis(e.g., all segmentations of a given organ). Examples of suchapplications are retrospective clinical trials and knowledge basedradiotherapy planning. In databases, structures are usually assigned aname or a label. Since these names are assigned and entered by a user,they may be ambiguous and/or incorrect. Accordingly, by running thequality checks on all structures, or, more efficiently, those that havebeen preselected based on their names, embodiments can assure that onlycorrect structures of the desired kind are selected.

Alternative embodiments may implement various other statistical shapemodels such as, for example, Active Shape Models, Active AppearanceModels, Morphable Models, Statistical Deformation Models, and DistanceMap-based shape models. In some alternative embodiments, the statisticalshape model-based checks can be based on any of these statisticalmethods aiming at representing the variety of shape and/or appearance ofan organ.

In some alternative embodiments, the shape model checks and/or theheuristic rules may be implemented with other representations of thestructures, such as, for example, surface meshes, volumetric meshes,implicit functions, images, or contour lines.

In further embodiments, automatic quality checks can be performed byimplementing the quality checks in radiotherapy contouring software. Inother embodiments, quality checks are provided in a quality reportwithin the contouring software. Other embodiment, provide quality checkswith live indication in a user interface of the contouring software. Oneembodiment identifies, displays, and navigates to individual regionswhere quality checks have failed. One embodiment provides semanticknowledge about organs in radiotherapy contouring software to allow fororgan-specific rule application. One embodiment uses statistical shapemodels for quality control (e.g., the statistical shape model of Kim,for example). One embodiment uses a statistical shape model inradiotherapy (e.g., the statistical shape model of Kim, for example).One embodiment uses a statistical test for shape modeling (e.g., thestatistical test of Kim, for example). One embodiment uses a statisticaltest in radiotherapy (e.g., the statistical test of Kim, for example).

It will be appreciated that the processes, systems, and sectionsdescribed above can be implemented in hardware, hardware programmed bysoftware, software instruction stored on a non-transitory computerreadable medium or a combination of the above. For example, a method forcan be implemented using a processor configured to execute a sequence ofprogrammed instructions stored on a non-transitory computer readablemedium. The processor can include, but not be limited to, a personalcomputer or workstation or other such computing system that includes aprocessor, microprocessor, microcontroller device, or is comprised ofcontrol logic including integrated circuits such as, for example, anApplication Specific Integrated Circuit (ASIC).

The instructions can be compiled from source code instructions providedin accordance with a programming language such as Java, C++, C#.net orthe like. The instructions can also comprise code and data objectsprovided in accordance with, for example, the Visual Basic™ language,LabVIEW, or another structured or object-oriented programming language.The sequence of programmed instructions and data associated therewithcan be stored in a non-transitory computer-readable medium such as acomputer memory or storage device which may be any suitable memoryapparatus, such as, but not limited to read-only memory (ROM),programmable read-only memory (PROM), electrically erasable programmableread-only memory (EEPROM), random-access memory (RAM), flash memory,disk drive and the like.

Furthermore, the modules, processes, systems, and sections can beimplemented as a single processor or as a distributed processor.Further, it should be appreciated that the steps mentioned above may beperformed on a single or distributed processor (single and/ormulti-core). Also, the processes, modules, and sub-modules described inthe various figures of and for embodiments above may be distributedacross multiple computers or systems or may be co-located in a singleprocessor or system.

The modules, processors or systems described above can be implemented asa programmed general purpose computer, an electronic device programmedwith microcode, a hard-wired analog logic circuit, software stored on acomputer-readable medium or signal, an optical computing device, anetworked system of electronic and/or optical devices, a special purposecomputing device, an integrated circuit device, a semiconductor chip,and a software module or object stored on a computer-readable medium orsignal, for example.

Embodiments of the method and system (or their sub-components ormodules), may be implemented on a general-purpose computer, aspecial-purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element, an ASIC or other integratedcircuit, a digital signal processor, a hardwired electronic or logiccircuit such as a discrete element circuit, a programmed logic circuitsuch as a programmable logic device (PLD), programmable logic array(PLA), field-programmable gate array (FPGA), programmable array logic(PAL) device, or the like. In general, any process capable ofimplementing the functions or steps described herein can be used toimplement embodiments of the method, system, or a computer programproduct (software program stored on a non-transitory computer readablemedium).

Furthermore, embodiments of the disclosed method, system, and computerprogram product may be readily implemented, fully or partially, insoftware using, for example, object or object-oriented softwaredevelopment environments that provide portable source code that can beused on a variety of computer platforms.

Alternatively, embodiments of the disclosed method, system, and computerprogram product can be implemented partially or fully in hardware using,for example, standard logic circuits or a very-large-scale integration(VLSI) design. Other hardware or software can be used to implementembodiments depending on the speed and/or efficiency requirements of thesystems, the particular function, and/or particular software or hardwaresystem, microprocessor, or microcomputer being utilized.

Features of the disclosed embodiments may be combined, rearranged,omitted, etc., within the scope of the invention to produce additionalembodiments. Furthermore, certain features may sometimes be used toadvantage without a corresponding use of other features.

It is thus apparent that there is provided in accordance with thepresent disclosure, an automatic segmentation and contour evaluationmethod implemented by a computer processing device. Many alternatives,modifications, and variations are enabled by the present disclosure.While specific embodiments have been shown and described in detail toillustrate the application of the principles of the present invention,it will be understood that the invention may be embodied otherwisewithout departing from such principles. Accordingly, Applicants intendto embrace all such alternatives, modifications, equivalents, andvariations that are within the spirit and scope of the presentinvention.

The invention claimed is:
 1. A method for detecting radiation therapysegmentation errors in one or more segments of an anatomical structureof a patient, comprising: generating one or more segments of theanatomical structure by delineating the anatomical structure onradiation therapy image slices obtained for the patient; and evaluatingthe one or more segments using one or more evaluation methods todetermine whether the one or more segments pass one or morepredetermined evaluation criteria, wherein the evaluating includesevaluating the one or more segments using a heuristic evaluation method,wherein the evaluating using the heuristic evaluation method comprises:evaluating the one or more segments using a set of heuristic rules; anddetermining whether at least one of the heuristic rules is violated,wherein, if at least one of the heuristic rules is violated, adetermination is made that an error in the segmentation is present,wherein the determining whether at least one of the heuristic rules isviolated includes evaluating the one or more segments using Booleanoperations and pixel/voxel counting.
 2. The method of claim 1, whereinthe evaluating is automatic.
 3. The method of claim 1, furthercomprising: generating binary images for the one or more segments;combining the binary images using at least one of a plurality of Booleanoperators, to thereby obtain a resulting binary image; and determiningwhether the resulting binary image satisfies the heuristic rule bycounting pixels/voxels in the resulting binary image.
 4. The method ofclaim 3, wherein a heuristic rule is violated if the number ofpixels/voxels is not within a predetermined range.
 5. The method ofclaim 4, further comprising displaying a signal alerting that asegmentation error is present.
 6. The method of claim 4, furthercomprising displaying a segmentation report including the results of thesegmentation evaluation.
 7. The method of claim 1, wherein thedetermining whether at least one of the heuristic rules is violatedfurther includes evaluating the one or more segments using a 3Dconnectivity labeling process.
 8. The method of claim 1, wherein theevaluating further includes evaluating the one or more segments using astatistical evaluation method, which comprises evaluating a shape of asegment and determining whether the shape of the segment is within apredetermined range of known shapes for the segment.
 9. The method ofclaim 8, further comprising generating a shape model based on aprobabilistic distribution of shape variations found in the range ofknown shapes for the segment, and performing a statistical test to checkif the shape of the segment is within the predetermined range.
 10. Themethod of claim 9, wherein the probabilistic distribution includes amultivariate normal distribution

(μ, Σ).
 11. The method of claim 10, wherein the performing of thestatistical check comprises evaluating how well the segment shape fitswithin the multivariate normal distribution using a probabilisticprincipal component analysis (PPCA).
 12. The method of claim 11, whereinif the shape of the segment does not fit within the multivariate normaldistribution within a predetermined value, it is determined that asegmentation error is present.
 13. The method of claim 1, wherein theevaluating is in real time.
 14. A system for automatically detectingerrors in a radiation therapy segmentation of an anatomical structure,comprising: a radiation therapy imaging device configured to generate orobtain one or more image slices containing the anatomical structure, theradiation therapy imaging device being further configured to generateone or more segments on the image slices by delineating the anatomicalstructure on the image slices; and a computer processing deviceconfigured to automatically evaluate the generated segments using one ormore evaluation methods, wherein the computer processing devicecomprises a database including a set of heuristic rules, wherein theautomatic evaluation includes checking whether one or more of thesegments fail at least one of the heuristic rules, wherein the computerprocessing device is further configured to generate binary images forthe segments and to combine the binary images using one or more Booleanoperators to obtain a combined binary image, and wherein the computerprocessing device is further configured to evaluate whether theresulting binary image complies with a heuristic rule using apixel/voxel counting process.
 15. The system of claim 14, furthercomprising displaying an error signal on a display device if thecomputer processing device determines that the resulting binary imagefails to comply with a heuristic rule.
 16. The system of claim 14,wherein the automatic evaluation further includes evaluating the one ormore segments using a statistical evaluation method, wherein thecomputer processing device is further configured to implement astatistical shape model to evaluate a shape of a segment and determinewhether the shape of the segment is within a predetermined range ofknown shapes for the segment.
 17. A method for detecting radiationtherapy segmentation errors in one or more segments of an anatomicalstructure, comprising: generating one or more segments of the anatomicalstructure by delineating the anatomical structure on radiation therapyimage slices obtained by imaging a patient; and evaluating the one ormore segments using a statistical evaluation method including:evaluating a shape of the segment; and determining whether the shape ofthe segment is within a predetermined range of known shapes for thesegment, wherein the evaluating further includes: generating a shapemodel based on a probabilistic distribution of shape variations found inthe range of known shapes for the segment; and performing a statisticaltest to check if the shape of the segment is within the predeterminedrange.
 18. The method of claim 17, wherein the probabilisticdistribution includes a multivariate normal distribution

(μ, Σ).
 19. The method of claim 18, wherein the performing of thestatistical check comprises evaluating how well the segment shape fitswithin the multivariate normal distribution using a probabilisticprincipal component analysis (PPCA).
 20. The method of claim 19, whereinif the shape of the segment does not fit within the multivariate normaldistribution within a predetermined value, it is determined that asegmentation error is present.